A General Architecture for Behavior Modeling of Nonlinear Power Amplifier using Deep Convolutional Neural Network

TL;DR

This paper introduces a low-complexity deep convolutional neural network architecture for accurately modeling the nonlinear behavior of power amplifiers in wireless systems, addressing computational challenges of traditional models.

Contribution

It proposes a novel DRVCNN-based framework that effectively models both single- and multi-carrier power amplifier nonlinearities with reduced computational cost.

Findings

Effective modeling of nonlinear PA behavior demonstrated

Applicable to single- and multi-carrier systems

Reduces computational complexity compared to traditional models

Abstract

Nonlinearity of power amplifier is one of the major limitations to the achievable capacity in wireless transmission systems. Nonlinear impairments are determined by the nonlinear distortions of the power amplifier and modulator imperfections. The Volterra model, several compact Volterra models and neural network models to establish a nonlinear model of power amplifier have all been demonstrated. However, the computational cost of these models increases and their implementation demands more signal processing resources as the signal bandwidth gets wider or the number of carrier aggregation. A completely different approach uses deep convolutional neural network to learn from the training data to figure out the nonlinear distortion. In this work, a low complexity, general architecture based on the deep real-valued convolutional neural network (DRVCNN) is proposed to build the nonlinear behavior of the power amplifier. With each of the multiple inputs equivalent to an input vector, the DRVCNN tensor weights are constructed from training data thanks to the current and historical envelope-dependent terms, I, and Q, which are components of the input. The effectiveness of the general framework in modeling single-carrier and multi-carrier power amplifiers is verified.